Spatial and Temporal Statistical Analysis of Water Quality Patterns in a Small Temperate Supply Reservoir

Abstract

Twenty-four years of spatial-temporal water quality data from three different sampling points at the surface were evaluated in Deer Creek Reservoir in Utah. The chosen sampling locations represent the lotic, transitional and lentic zones of a typical man-made lake. The time frame included data collected before and after the completion of the Jordanelle Reservoir (1987)(1988)(1989)(1990)(1991)(1992), upstream of Deer Creek. On average chlorophyll-a and phosphorus levels have dropped since 1984 and dissolved oxygen levels have remained the same. We used stepwise variable selection and multivariable regression to fit chlorophyll-a on climatological, hydrological, and water quality parameters. Analyses of variance (ANOVA) were used to quantify spatial and temporal variation. Significant spatial variation in chlorophyll-a concentration was found to be 92% higher on average in the lotic zone than the lentic zone. The regression model was also used to evaluate future water quality effects produced by different climatic change scenarios. Chlorophyll-a concentrations were used as water quality indicators to assess the in-reservoir effects of climate variation produced by meteorological changes. The model predicted an inverse relationship between air temperature and chlorophyll-a concentrations. Our findings were validated with results obtained from a computational water quality model that found that the statistical model showed similar trends of chlorophyll-a.

Population-based Analysis of Alzheimer’s Disease Risk Alleles Implicates Genetic Interactions

“…To test for and avoid overfitting, we included three random variables while generating the optimized model. These variables were generated randomly with respect to all genotype and phenotype data in our study and were included to provide evidence that the selected variables provide meaningful information (20). While the absence of all random variables in the model does not guarantee the model was not overfit, it does suggest the included variables provide useful diagnostic information.…”